diff --git a/stud/gabdulkhakov/lab1-02.csv b/stud/gabdulkhakov/lab1-02.csv
new file mode 100644
index 0000000..b51d696
--- /dev/null
+++ b/stud/gabdulkhakov/lab1-02.csv
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diff --git a/stud/gabdulkhakov/lab1.ipynb b/stud/gabdulkhakov/lab1.ipynb
new file mode 100644
index 0000000..758debd
--- /dev/null
+++ b/stud/gabdulkhakov/lab1.ipynb
@@ -0,0 +1,925 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "416671c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ae5184c",
+ "metadata": {},
+ "source": [
+ "## 1. Загрузить данные из файла.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "89c16289",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " X \n",
+ " Y \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1.323232 \n",
+ " 1443.073978 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2.373737 \n",
+ " 1779.828149 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 8.878788 \n",
+ " 11710.737921 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 8.555556 \n",
+ " 10962.941786 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2.858586 \n",
+ " 3494.724975 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x_column = 'X'\n",
+ "y_column = 'Y'\n",
+ "\n",
+ "fig, (ax1) = plt.subplots(1, figsize=(9, 6))\n",
+ "\n",
+ "ax1.scatter(df[x_column], df[y_column], alpha=0.7, color='blue')\n",
+ "ax1.set_xlabel(x_column)\n",
+ "ax1.set_ylabel(y_column)\n",
+ "ax1.set_title('Диаграмма рассеяния')\n",
+ "ax1.grid(True, alpha=0.3)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a58999a9",
+ "metadata": {},
+ "source": [
+ "## 3. Разбить данные на обучающую и тестовую выборки."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "7749313c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = df[[x_column]]\n",
+ "y = df[y_column]\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76690306",
+ "metadata": {},
+ "source": [
+ "## 4. Выбрать модель регрессии."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "448a82c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = LinearRegression()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d5b315f",
+ "metadata": {},
+ "source": [
+ "## 5. Обучить модель регрессии на обучающих данных."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "68b0980a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " fit_intercept \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " copy_X \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " tol \n",
+ " 1e-06 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " n_jobs \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " positive \n",
+ " False \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(X, y, color='blue', label='Начальные данные')\n",
+ "plt.scatter(X_test, y_test_pred, color='red', label='Предсказанные данные')\n",
+ "plt.xlabel('X')\n",
+ "plt.ylabel('Y')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "bb8da46b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "R2: 0.8013765670559003\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('R2:', r2_score(y_test, y_test_pred))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "930a1c4e",
+ "metadata": {},
+ "source": [
+ "Метрика имеет неплохое значение, для этого набора данных. Рассмотрим другие метрики"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "21ad4795",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MSE: train = 1726452.6703531637, test = 878266.8452145274\n",
+ "MAE: train = 1050.7518155844155, test = 773.634556461533\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_mse = mean_squared_error(y_train, y_train_pred)\n",
+ "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
+ "\n",
+ "test_mse = mean_squared_error(y_test, y_test_pred)\n",
+ "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
+ "\n",
+ "print(f\"MSE: train = {train_mse}, test = {test_mse}\")\n",
+ "print(f\"MAE: train = {train_mae}, test = {test_mae}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46cff092",
+ "metadata": {},
+ "source": [
+ "Ошибки оказались очень большими, потому что модель не может в полной мере описать данные"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Global Environment",
+ "language": "python",
+ "name": "my_global_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}